In diesel engine and other applications, very often control designers desire to create “inferential sensor” signals. These are signals in which a variable that is difficult or expensive to measure directly is inferred by combining the information received from one or more sensors, each measuring a different property. This set of sensor signals is input to an algorithm from which the desired quantity is inferred. A problem faced by the designer of inferential sensors is that they require significant engineering time and test cell measurements in order to calibrate the algorithms to achieve sufficient accuracy.
This process is illustrated in graphic form in FIG. 1, wherein engine control unit (ECU) calibration personnel determine values of the calibration parameter (Θ in FIG. 1) such that the inferred value (Z in FIG. 1) is sufficiently accurate. In using the process of FIG. 1, in many cases, the calibration of inferential sensing algorithms is achieved by manual manipulation of the calibration parameter, that is, manipulating the numerical values of theta (Θ) until an acceptable performance is observed. This manual or semi-manual process requires a significant amount of expensive engineering and engine test cell time.
Specifically, to accomplish this, an engineer equips an engine with a set of sensors and makes the signals of these sensors (y in FIG. 1) available to an engine control unit. The engineer further equips the engine to measure a desired engine output or variable (zmeas) using a sensor (which may be expensive, complex, not too robust, and/or not too accurate). The engineer then measures the variables y and zmeas at several operating points of the engine. As noted, manual or semi-manual techniques are used to calculate the numerical values for theta such that the modeled function z=f(y, theta) is close to the measured values (zmeas). Put another way, the norm of the difference between the estimated z and the measured zmeas is minimized. This process is then difference between the desired z and measured z is minimized.
Because this is a painstaking, time consuming, less than accurate, and expensive process, designers often “live with” substandard calibration, or spend significant costs on obtaining the desired accuracy.